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Join marketing, finance, CRM, and product data inside the same warehouse.
Metrics availables
Dimensions availables
Add the database host and credentials once. Catchr checks the connection so your exports start with a reachable destination.
Choose the fields, initial history, partition field, and recurring schedule for the job. Catchr refreshes the configured import window in your PostgreSQL table.
This example summarizes product impressions, clicks, conversions, and click-through rate.
Each connector has its own entities, fields, historical limits, and reporting uses. Open a source page for the details that belong to that platform.
Answers about PostgreSQL setup, available source data, schedules, table updates, and SQL use cases.
Register Google Merchant Center, save the PostgreSQL connection details, and pair both systems in a datastream. The associated job previews and writes the selected columns to "accounts".
Select concrete Google Merchant Center fields such as Average Orders Sales, Average Orders Value, Clicks, and Account Status - Account Level Issue Country. You can rename the chosen columns, preview the result, and write them to PostgreSQL tables such as accounts, products, and product groups.
The "accounts" job can begin with a historical Google Merchant Center fetch and continue on a recurring schedule. Available history and frequency depend on the source API and your plan.
Select a valid date field when configuring the Google Merchant Center job for "accounts". Catchr deletes and rewrites the scheduled window, limiting duplicate rows across overlapping runs.
With Google Merchant Center data, a PostgreSQL query can summarize product impressions, clicks, conversions, and click-through rate. Turn Average Orders Sales and Average Orders Value into a stable PostgreSQL view that analysts and reporting tools can query repeatedly.
Yes. Connect an existing PostgreSQL database first. The Catchr job creates and populates the destination table you configure inside that database.
Yes. Create a separate datastream and job for each source, write each export to its own table, then combine the data downstream with SQL, views, or your BI tool.
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